Generative AI on SpectrumNet: An Open Benchmark of Multiband 3D Radio Maps

TL;DR

This paper introduces SpectrumNet, a comprehensive multiband 3D radio map dataset designed to improve generative AI models for constructing high-resolution wireless signal coverage maps across diverse terrains, spectrum bands, and heights.

Contribution

The paper presents the largest multiband 3D radio map dataset, SpectrumNet, enabling better generalization of generative models for radio map construction in varied scenarios.

Findings

SpectrumNet dataset covers 3D space, 5 frequency bands, 11 terrains, 3 climates.

Baseline models trained on SpectrumNet show improved generalization.

The dataset facilitates future research on generative models for radio mapping.

Abstract

Radio map is an efficient demonstration for visually displaying the wireless signal coverage within a certain region. It has been considered to be increasingly helpful for the future sixth generation (6G) of wireless networks, as wireless nodes are becoming more crowded and complicated. However, the construction of high resolution radio map is very challenging due to the sparse sampling in practical systems. Generative artificial intelligence (AI), which is capable to create synthetic data to fill in gaps in real-world measurements, is an effective technique to construct high precision radio maps. Currently, generative models for radio map construction are trained with two-dimension (2D) single band radio maps in urban scenario, which has poor generalization in diverse terrain scenarios, spectrum bands, and heights. To tackle this problem, we provide a multiband three-dimension (3D) radio map dataset with consideration of terrain and climate information, named SpectrumNet. It is the largest radio map dataset in terms of dimensions and scale, which contains the radio map of 3 spacial dimensions, 5 frequency bands, 11 terrain scenarios, and 3 climate scenarios. We introduce the parameters and settings for the SpectrumNet dataset generation, and evaluate three baseline methods for radio map construction based on the SpectrumNet dataset. Experiments show the necessity of the SpectrumNet dataset for training models with strong generalization in spacial, frequency, and scenario domains. Future works on the SpectrumNet dataset are also discussed, including the dataset expansion and calibration, as well as the extended studies on generative models for radio map construction based on the SpectrumNet dataset.